https://github.com/bhklab/predictio-uv-dist
Distributed univariable predictive modelling for Immuno-Oncology response
Science Score: 26.0%
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Low similarity (13.4%) to scientific vocabulary
Repository
Distributed univariable predictive modelling for Immuno-Oncology response
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 11
- Releases: 0
Metadata Files
docs/README.md
Distributed univariable predictive modelling for Immuno-Oncology response
Authors: Farnoosh Abbas Aghababazadeh, Nasim Bondar Sahebi
Contact: farnoosh.abbasaghababazadeh@uhn.ca, nasim.bondarsahebi@uhn.ca
Description: A distributed framework for univariable predictive modeling of Immuno-Oncology (IO) response, enabling analysis across multiple centers without sharing patient-level data.
Project Overview
This repository implements a distributed framework for evaluating the predictive value of RNA-based signatures in response to Immuno-Oncology (IO) therapies. It supports:
- Center-specific signature scoring and modeling (e.g., OS, PFS, response)
- Strict data privacy (no sharing of raw or patient-level data)
- Centralized meta-analysis of effect sizes across datasets
- Modular, reproducible pipeline built with Pixi, Nextflow, and R
Set Up
Prerequisites
Pixi is required to run this project. If you haven't installed it yet, follow these instructions
Installation
```bash
Clone the repository
git clone https://github.com/bhklab/predictio-uv-dist.git cd predictio-uv-dist
Install dependencies via Pixi
pixi install ```
Repository Structure
predictio-uv-dist/
├── config/ # YAML config files for each dataset and center
├── data/ # Raw, processed, and results directories
├── workflow/ # Scripts and Nextflow pipeline for analysis
├── docs/ # MkDocs-based project documentation
│ └── README.md # Documentation index and setup instructions
└── pixi.toml # Pixi environment specification
Documentation
Full documentation, including usage instructions, data setup, config templates, and pipeline stages, will be available in the docs/ folder or via published GitHub Pages.
Start by downloading and organizing the raw input datasets as described in data/rawdata/README.md.
For data download and processing, please refer to the univariable repository:
🔗 https://github.com/bhklab/PredictIO-UV-Dist
Owner
- Name: BHKLAB
- Login: bhklab
- Kind: organization
- Location: Toronto, Ontario, Canada
- Website: http://www.pmgenomics.ca/bhklab/
- Repositories: 168
- Profile: https://github.com/bhklab
The Haibe-Kains Laboratory @ Princess Margaret Cancer Centre
GitHub Events
Total
- Issues event: 3
- Issue comment event: 1
- Push event: 116
- Pull request event: 1
- Create event: 1
Last Year
- Issues event: 3
- Issue comment event: 1
- Push event: 116
- Pull request event: 1
- Create event: 1
Dependencies
- amannn/action-semantic-pull-request v5.0.2 composite
- peter-evans/create-or-update-comment v4 composite
- actions/checkout v3 composite
- actions/github-script v6 composite
- actions/checkout v3 composite
- prefix-dev/setup-pixi v0.8.1 composite
- GoogleCloudPlatform/release-please-action v4 composite
- actions/checkout v4.2.0 composite